DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments and amendments filed on 02/25/2026 with respect to the previous 35 U.S.C. 101 rejection has been fully considered and is unpersuasive.
With respect to the previous 35 U.S.C. 101 rejection of claim 13, The limitations of claim 13 do not overcome the 35 U.S.C. 101 rejection. Applicant claim 13 recites “…the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement…” under its broadest reasonable interpretation, the above limitations are directed to a judicial exception namely a mental process under 35 U.S.C. 101 under the broadest reasonable interpretation the claimed fuzzy inference rules determining whether lane change conditions are satisfied and issuing a lane change recommendation amount to observation, evaluation and decision making processes that can practically be performed in the human mind or with pen and paper. The additional recitation of a vehicle, sensor and fuzzy inference system merely provides a generic technological environment for implementing the abstract idea and does not integrate the exception into a practical application or amount to significantly more. which constitutes a mental process as enumerated in Section I of the 2018 patent Eligibility Guidance (PEG). Further the claimed sensor merely gathers data for use in the abstract analysis while the vehicle merely applies the result of the recommendation. Such extra elements constitute insignificant extra-solution activity and do not improve vehicle technology, sensor technology or computer functionality itself. Accordingly, the claims do not alter the fundamental nature of the claimed concept as these elements merely provide a technological environment in which the abstract idea is performed.
If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind, then it falls within the “mental processes” grouping of abstract ideas in the 2019 PEG regardless of whether a computer is used to perform the steps more efficiently. Accordingly, these claims recite an abstract idea and in addition to sensors being used are additional elements as they amount to necessary mere data gathering or a tool to receive the data being gathered to perform the abstract idea, which is an insignificant extra- solution activity to the judicial exception.
The same updated analysis based on the new 2019 Patent Eligibility Guidance (2019 PEG) applies to the newly added claimed limitations as discussed in the previous office action.
As a result, Step 2A Prong 1 determines if a claim is directed to those grouping and subgroupings along with an explanation of why it is directed to such.
“First, the rejection should identify the judicial exception (i.e., abstract idea enumerated in Section I of the 2019 PEG, laws of nature, or a natural phenomenon) by referring to what is recited (i.e., set forth or described) in the claim and explaining why it is considered to be an exception (Step 2A Prong One). There is no requirement for the examiner to provide further support, such as publications or an affidavit or declaration under 37 CFR 1.104(d)(2), for the conclusion that a claim recites a judicial exception.”
“For abstract ideas, the rejection should explain why a specific limitation(s) recited in the claim falls within one of the enumerated groupings of abstract ideas (i.e., mathematical concepts, mental processes, or certain methods of organizing human activity) or provide a justification for why a specific limitation(s) recited in the claim is being treated as an abstract idea if it does not fall within the enumerated groupings of abstract ideas in accordance with the “tentative abstract idea” procedure in the 2019 PEG.”
In the Non-Final mailed 02/25/2026 examiner performs the analysis and clarifies that “the abstract idea noted in the independent claims…are directed to a “Mental Processes.” And/or “Mathematical Processes” Hence, examiner has indicated that these identified limitations are directed to “…the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement…” and has provided a justification for why these limitations fall within one of the enumerated groupings of abstract ideas (i.e. concepts performed in the human mind). This is sufficient under the guidelines of the 2019 PEG and October 2019 Update as cited above. Also, the claims do not provide any control step to control the commercial vehicle to change lanes or to stay on the same lane if it is not safe to perform a lane change and in addition to the claim limitations only provide lane change recommendations according to the relative distance between the vehicle and an object in front of the vehicle. Accordingly, it seems reasonable for the examiner to group the abstract idea under “Mental processes.” as enumerated in Section I of the 2019 PEG.
Prong Two:
With respect to Step 2A, prong two, Integration into a practical application requires an additional element(s) or a combination of additional elements in the claim to apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
Applicant argues that the claimed limitations integrate the abstract idea into a practical application by the fuzzy inference system in communication with the sensor, the fuzzy inference system being configured to receive the first input, the second input, and the third input from the sensor and generate an output comprising a lane change recommendation based on the first input, the second input, and the third input. However, the additional elements merely amount to instructions to implement the abstract idea on a computer.
The recited acts of a fuzzy inference system and sensors constitute insignificant extra-solution activity, such as data gathering and output, which do not integrate the judicial exception into practical application. See MPEP 2106.05(f).
Further the claims do not recite any limitation that controls the vehicle itself, alters vehicle operation, or change physical driving behavior. Instead, the claims merely recommend or present route information, leaving the final decision to the driver. Such route observation and lane change recommendation can be performed mentally and does not amount a technological improvement.
Limitations that are not indicative of integration into a practical application are those that are mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea.-see MPEP 2106.05(f). Claiming issuing signals inherent with applying any improvement to the judicial exception itself on a computer does not provide an inventive concept. The claims do not integrate the judicial exception into a practical application. The courts found that “… if a patent’s recitation of a computer amounts to a mere instruction to ‘implement[t]’ an abstract idea ‘on . . . a computer,’ that addition cannot impart patent eligibility.” Alice Corp., 134 S.Ct. at 2358. The claimed invention does not indicate that specialized computer hardware is necessary to implement the claimed systems, similar to the claims at issue in Alice Corp. See Alice Corp., 134 S.Ct. at 2360 (determining that the hardware recited in the claims was “purely functional and generic,” and did not “offer a meaningful limitation beyond generally linking the use of the [method] to a particular technological environment, that is, implementation via computers”). The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem. Also, limiting the use of an abstract idea “‘to a particular technological environment’ does not confer patent eligibility as this cannot be considered an improvement to computer or technology and so cannot be “significantly more.” Examiner notes Applicant’s citing to Enfish, LLC v Microsoft corp, 822 F.3d 1327, 1336, 118 USPQ2d 1684, 1689 (Fed. Cir. 2016). Like the improved systems claimed in Enfish, these claims recite a specific improvement over prior systems, resulting in an improved determination of the priority evacuation area and controlling the vehicle to perform the evacuation plan” The present case is different: the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem. Also, limiting the use of an abstract idea “‘to a particular technological environment’ does not confer patent eligibility as this cannot be considered an improvement to computer or technology and so cannot be “significantly more.” Therefore, Enfish does not apply here. The Court gave examples, which included an improvement to another technology or technical field; improvement to the function of the computer itself; or some other meaningful limitation beyond generally linking the use of an abstract idea to a particular technological environment. Such as in Diamond v. Diehr, the claims were found statutory in which the Arrhenius equation is used to improve a process of controlling the operation of a mold in curing rubber parts. Examiner submits that under the current 35 U.S.C. 101 examining practice, the existence of such novel features would still not cure the deficiencies with respect to the abstract idea. See for example: Ultramercial, Inc. v. Hulu, LLC, 112 USPQ2d 1750, U.S. Court of Appeals Federal Circuit, No. 2010-1544, Decided November 14, 2014, 2014 BL 320546, 772 F.3d 709, Page 1754 last two ¶: “We do not agree with Ultramercial that the addition of merely novel or non-routine components to the claimed idea necessarily turns an abstraction into something concrete.” The instant claims are different, the focus of the claims is not on such an improvement in computers as tools, but on certain independently abstract ideas that use computers as tools. The claims here are not directed to a specific improvement to computer functionality nor an inventive solution to any computer specific problem. Lastly, dependent claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements are simply steps performed by a generic computer. The claim merely amounts to the application or instructions to apply the abstract idea on a processor, and is considered to amount to nothing more than requiring a generic processor to merely carry out the abstract idea itself.
With respect to Step 2B the claim is analyzed to determine if there are additional claim limitations that individually, or as an ordered combination, ensure that the claim amounts to significantly more than the abstract ideas (whether claim provides inventive concept). As discussed above, the recitation of the claimed limitations amounts to mere instructions to implement the abstract idea on a processor (using the processor as a tool to implement the abstract idea). Taking the additional elements individually and in combination, the processor at each step of the process performs purely generic computer functions. As such, there is no inventive concept sufficient to transform the claimed subject matter into a patent-eligible application The same analysis applies here, i.e., mere instructions to apply an exception using a generic computer component cannot integrate a judicial exception into a practical application at or provide an inventive concept.
For these reasons the rejection under 35 U.S.C. § 101 directed to non-statutory subject matter set forth in this office action is maintained.
Upon further consideration of the prior art of record, including Song, Applicant’s amendments and remarks filed on 02/25/2026 with respect to previous claim rejections under 35 U.S.C. 103 have been fully considered but are not persuasive.
With respect to the previous 35 U.S.C. 103 rejections of claims 13 and 19 Applicant argue the cited art of record, Song et al (US 2024/0351615 A1), (hereinafter Song) in further view of Tokoro (US 6,323,802 B1), fails to explicitly disclose all of the recited features of the amended claim 13 and 19 (see response pages 10-11), specifically “the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement".
However, Examiner respectfully disagrees. As Song teaches “the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement”, as understood in at least (see Song ¶ 80 and 184-188) “A lane-changing willingness output model may be established based on fuzzy logic. First, an input and an output of the fuzzy logic-based lane-changing willingness model may be designed. As shown in FIGS. 5(a), 5(b), and 5(c), a membership function of each of the lane-changing necessity, the lane-changing safety, and the lane-changing willingness may be constructed. Fuzzy sets for the input, i.e., the lane-changing necessity and the lane-changing safety may be {small, relatively small, medium, relatively large, large}, and the fuzzy set for the output, i.e., the lane-changing willingness, may be {weak, relatively weak, medium, relatively strong, strong}. Finally, a lane-changing willingness φLC may be obtained by performing a defuzzification operation according to a fuzzy rule table (e.g., as shown in Table 1) and a center of mass technique. Recommended fuzzy set classifications and fuzzy rule formulations are provided in Table 1 but not limited to the forms in the table”, “the processor 110 may initially establish the lane-changing willingness output model based on fuzzy logic and store the lane-changing willingness output model in the storage 140. The fuzzy logic is a process that uses a fuzzy set and a fuzzy rule to determine a parameter (e.g., the lane-changing willingness) related to lane-changing decision-making”, “A membership degree refers to a degree to which an element belongs to the corresponding fuzzy set. In some embodiments, the membership degree is a numerical value between 0 and 1” and “the processor 110 may identify factors that influence the lane-changing willingness, such as the lane-changing necessity, the lane-changing safety, a surrounding vehicle behavior, a road condition, or the like. For each factor, a fuzzy set may be defined, for example, dividing the lane-changing necessity into elements such as “small,” “relatively small,” “medium,” “relatively large,” “large,” or the like. The processor 110 may establish membership functions based on each element in the fuzzy set to describe the membership degree of the element in the corresponding fuzzy set” regarding obtaining vehicle and surrounding vehicle information through an on-board sensor and determining lane-changing necessity and lane-changing safety based on the obtained inputs. Song teaches establishing a lane-changing willingness output model based on fuzzy logic and generating a lane-changing willingness output through a fuzzy rule table and defuzzification operation. Also the claimed “if statement” and “then statement,” the fuzzy rule table and fuzzy rule formulations of the reference reasonably correspond to conditional decision logic under the broadest reasonable interpretation, where input conditions associated with lane-changing necessity and lane-changing safety correspond to the claimed “if statement,” and the resulting lane-changing willingness output corresponds to the claimed “then statement” describing the lane change recommendation to be taken. Further, the Song teaches mapping input variables to output variables corresponding to lane-changing willingness and performing a subsequent lane-changing decision-making process when the lane-changing willingness exceeds a threshold. Additionally, Song teaches that the lane-changing willingness output model is used to determine the tendency of a vehicle to perform a lane change and that fuzzy rules are used to determine parameters related to lane-changing decision-making. Thus, relaying a lane-change recommendation to the vehicle and applying the recommendation by performing a lane change when the determined conditions satisfy the fuzzy rule criteria, while otherwise refraining from performing the lane change operation.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 13-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
In particular, claims are directed to a judicial exception (abstract idea) without significantly more.
Re Claim 13:
Claim 13 recites:
An advanced driving assistance system comprising a vehicle having a fuzzy inference system and a sensor attached to the vehicle, the sensor being configured to obtain a first input comprising a lane detection, a second input comprising a distance, and a third input comprising a relative distance, wherein: the fuzzy inference system is a lane change logic set of fuzzy logic rules, the fuzzy inference system in communication with the sensor, the fuzzy inference system being configured to receive the first input, the second input, and the third input from the sensor and generate an output comprising a lane change recommendation based on the first input, the second input, and the third input, the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement; the lane detection is the detection of a parallel lane into which the vehicle may move through a change of lanes; the distance is a measurement between the vehicle and an object in front of the vehicle; and the relative distance is a measurement of a rate of change of a second distance between the vehicle and an object detected in the parallel lane.
Under Step 1 Claim 13 is a system claim same as claims 14-17.
Under Step 2A -Prong 1:
The identified claim limitations that recite an abstract idea fall within the enumerated groupings of abstract ideas in Section 1 of the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019. These fall under mental process.
Claim 1 recites “An advanced driving assistance system comprising a vehicle having a fuzzy inference system and limitations, under their broadest reasonable interpretation, cover performance of the limitation as mental processes. As a passenger/driver, could observe the lane ahead of the vehicle and also the distance of the vehicle in front of the own vehicle in addition to the relative distance to the vehicle next to the own vehicle in the adjacent lane and then using a pen and paper do the calculation to see if the own vehicle is safe to perform a lane change or not. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as a concept performed in the human mind, then it falls within the “Mental Processes” or “Mathematical Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea. Claims 13-20 are also abstract for similar reasons.
Under Step 2A - Prong 2; the claims recite the additional element of “the fuzzy inference system in communication with the sensor, the fuzzy inference system being configured to receive the first input, the second input, and the third input from the sensor, the sensor being configured to obtain a first input comprising a lane detection” steps is not more than adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, these additional elements, when considered separately and as an ordered combination, does not integrate the abstract idea without a practical application because they do not impose any meaningful limits on practicing the abstract idea and are at a high level of generality. Therefore, claim 13 is directed to an abstract idea without a practical application.
Under Step 2B:
The claims do not include additional elements that are sufficient to amount to significantly more that the judicial exception because, when considered separately and as an ordered combination, they do not add significantly more (also known as an “inventive concept”) to the exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a computer hardware amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Accordingly, these additional elements, do not change the outcome of the analysis, when considered separately and as an ordered combination. Thus, claims 13-20 are not patent eligible.
Therefore, the method claim 19 and the system claim 20 are rejected under the same
rationales used in the rejections of claim 1 outlined above.
Dependent claims 14-17 Dependent claims further define the abstract idea that is present in their respective independent claim 1 and thus correspond to Mental Processes and hence are abstract for the reasons presented above. The dependent claims do not include any additional elements that integrate the abstract idea into a practical application or are sufficient to amount to significantly more than the judicial exception when considered both individually and as an ordered combination. Therefore, the dependent claims are directed to an abstract idea. Thus, the claims 13-17 and 19-20 are not patent-eligible.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 13, 16-17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable in view of Song et al (US 2024/0351615 A1), (hereinafter Song) in further view of Tokoro (US 6,323,802 B1).
Regarding claim 13, Song discloses an advanced driving assistance system comprising a vehicle having a fuzzy inference system and a sensor attached to the vehicle, the sensor being configured to obtain a first input comprising a lane detection, a second input comprising a distance, and a third input comprising a relative distance (see Song paras “0014”, “0078”, “0146”, “0162” and “0167-0171” “establishing a prior probability distribution of a vehicle driving style of a side vehicle, including: obtaining vehicle driving data through an intelligent networked roadside sensor, and recording and counting the prior probability distribution of the vehicle driving style under different time periods and different road sections, wherein the vehicle driving style of the side vehicle includes an aggressive driving style and a non-aggressive driving style; step 2, outputting a lane-changing willingness through a lane-changing willingness determination module, including: collecting vehicle information of a specified vehicle (SV) and a surrounding vehicle through an on-board sensor, determining an original predetermined distance and a lane-changing predetermined distance, determining a lane-changing necessity and a lane-changing safety through a cumulative distribution function constructed by introducing an expectation and a variance, establishing a lane-changing willingness output model based on fuzzy logic” and “The original predetermined distance refers to a distance between the specified vehicle and a leading vehicle on the current lane... For example, the original predetermined distance may be a minimum distance between reference points of the specified vehicle and reference points of the leading vehicle at various future moments. As another example, the original predetermined distance may include distances between the reference points of the specified vehicle and the reference points of the leading vehicle at various future moments. The reference points may be manually preset values or system default values, such as the midpoint of the vehicle's front.”),
wherein: the fuzzy inference system is a lane change logic set of fuzzy logic rules, the fuzzy inference system in communication with the sensor, the fuzzy inference system being configured to receive the first input, the second input, and the third input from the sensor (see Song paras “0014”, “0078”, “0126-0127”, “0131-0133”, “0146”, “0162” and “0167-0171” “establishing a prior probability distribution of a vehicle driving style of a side vehicle, including: obtaining vehicle driving data through an intelligent networked roadside sensor, and recording and counting the prior probability distribution of the vehicle driving style under different time periods and different road sections, wherein the vehicle driving style of the side vehicle includes an aggressive driving style and a non-aggressive driving style; step 2, outputting a lane-changing willingness through a lane-changing willingness determination module, including: collecting vehicle information of a specified vehicle (SV) and a surrounding vehicle through an on-board sensor, determining an original predetermined distance and a lane-changing predetermined distance, determining a lane-changing necessity and a lane-changing safety through a cumulative distribution function constructed by introducing an expectation and a variance, establishing a lane-changing willingness output model based on fuzzy logic” and “The original predetermined distance refers to a distance between the specified vehicle and a leading vehicle on the current lane... For example, the original predetermined distance may be a minimum distance between reference points of the specified vehicle and reference points of the leading vehicle at various future moments. As another example, the original predetermined distance may include distances between the reference points of the specified vehicle and the reference points of the leading vehicle at various future moments. The reference points may be manually preset values or system default values, such as the midpoint of the vehicle's front.”),
and generate an output comprising a lane change recommendation based on the first input, the second input, and the third input (see Song paras “0014-0017”, “0077-0079”, “0158-0159”, “0162” and “0184-0186” “The lane-changing necessity refers to a result of evaluating the need for a lane-changing action based on a traffic condition and a driving requirement of the specified vehicle. In some embodiments, the lane-changing necessity may be expressed as a numerical value (e.g., a necessity degree, a necessity value, or the like), a grade (e.g., a necessity level), or the like.”),
the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement (see Song paras “0084” and “0184-0186” “A lane-changing willingness output model may be established based on fuzzy logic. First, an input and an output of the fuzzy logic-based lane-changing willingness model may be designed. As shown in FIGS. 5(a), 5(b), and 5(c), a membership function of each of the lane-changing necessity, the lane-changing safety, and the lane-changing willingness may be constructed. Fuzzy sets for the input, i.e., the lane-changing necessity and the lane-changing safety may be {small, relatively small, medium, relatively large, large}, and the fuzzy set for the output, i.e., the lane-changing willingness, may be {weak, relatively weak, medium, relatively strong, strong}. Finally, a lane-changing willingness φLC may be obtained by performing a defuzzification operation according to a fuzzy rule table (e.g., as shown in Table 1) and a center of mass technique. Recommended fuzzy set classifications and fuzzy rule formulations are provided in Table 1 but not limited to the forms in the table”, “the processor 110 may initially establish the lane-changing willingness output model based on fuzzy logic and store the lane-changing willingness output model in the storage 140. The fuzzy logic is a process that uses a fuzzy set and a fuzzy rule to determine a parameter (e.g., the lane-changing willingness) related to lane-changing decision-making”, “A membership degree refers to a degree to which an element belongs to the corresponding fuzzy set. In some embodiments, the membership degree is a numerical value between 0 and 1” and “the processor 110 may identify factors that influence the lane-changing willingness, such as the lane-changing necessity, the lane-changing safety, a surrounding vehicle behavior, a road condition, or the like. For each factor, a fuzzy set may be defined, for example, dividing the lane-changing necessity into elements such as “small,” “relatively small,” “medium,” “relatively large,” “large,” or the like. The processor 110 may establish membership functions based on each element in the fuzzy set to describe the membership degree of the element in the corresponding fuzzy set” regarding obtaining vehicle and surrounding vehicle information through an on-board sensor and determining lane-changing necessity and lane-changing safety based on the obtained inputs. Song teaches establishing a lane-changing willingness output model based on fuzzy logic and generating a lane-changing willingness output through a fuzzy rule table and defuzzification operation. Also the claimed “if statement” and “then statement,” the fuzzy rule table and fuzzy rule formulations of the reference reasonably correspond to conditional decision logic under the broadest reasonable interpretation, where input conditions associated with lane-changing necessity and lane-changing safety correspond to the claimed “if statement,” and the resulting lane-changing willingness output corresponds to the claimed “then statement” describing the lane change recommendation to be taken. Further, the Song teaches mapping input variables to output variables corresponding to lane-changing willingness and performing a subsequent lane-changing decision-making process when the lane-changing willingness exceeds a threshold. Additionally, Song teaches that the lane-changing willingness output model is used to determine the tendency of a vehicle to perform a lane change and that fuzzy rules are used to determine parameters related to lane-changing decision-making. Thus, relaying a lane-change recommendation to the vehicle and applying the recommendation by performing a lane change when the determined conditions satisfy the fuzzy rule criteria, while otherwise refraining from performing the lane change operation.),
the distance is a measurement between the vehicle and an object in front of the vehicle (see Song paras “0014”, “0078”, “0146”, “0162” and “0167-0171” “The original predetermined distance refers to a distance between the specified vehicle and a leading vehicle on the current lane... For example, the original predetermined distance may be a minimum distance between reference points of the specified vehicle and reference points of the leading vehicle at various future moments. As another example, the original predetermined distance may include distances between the reference points of the specified vehicle and the reference points of the leading vehicle at various future moments. The reference points may be manually preset values or system default values, such as the midpoint of the vehicle's front.”),
but Song fails to explicitly teach the lane detection is the detection of a parallel lane into which the vehicle may move through a change of lanes and the relative distance is a measurement of a rate of change of a second distance between the vehicle and an object detected in the parallel lane.
However, Tokoro teaches the lane detection is the detection of a parallel lane into which the vehicle may move through a change of lanes and the relative distance is a measurement of a rate of change of a second distance between the vehicle and an object detected in the parallel lane (see Tokoro col 7, lines 20-27 “the object on the currently running lane is a ghost is determined using the fuzzy inference based on the parameters of the distance difference, the relative velocity difference, the angle, etc. between the detected object image on the currently running lane and the detected object image on the adjacent lane”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Song for methods for cooperative decision-making on lane-changing behaviors of autonomous vehicles based on Bayesian game “to carry out the object detection process without interruption and determine that the first detected object image is not a ghost, with a higher priority than the other determination conditions” as taught by Tokoro (col 7, lines 20-27) in order to improve safety for the vehicle and avoid any collision.
Regarding claim 16, Song discloses wherein each of the first input, the second input, and the third input comprises three membership functions that are triangular or trapezoidal in nature, and wherein the output comprises four membership functions (see Song paras “0182-0191” and “0251” “the processor 110 may identify factors that influence the lane-changing willingness, such as the lane-changing necessity, the lane-changing safety, a surrounding vehicle behavior, a road condition, or the like. For each factor, a fuzzy set may be defined, for example, dividing the lane-changing necessity into elements such as “small,” “relatively small,” “medium,” “relatively large,” “large,” or the like. The processor 110 may establish membership functions based on each element in the fuzzy set to describe the membership degree of the element in the corresponding fuzzy set. The membership functions may be linear, triangular, trapezoidal, or any other shape. For example, for the membership function of “high lane-changing necessity,” if the lane-changing necessity is 1, the value of the membership function is equal to or close to 1, if the lane-changing necessity is outside this range, the membership degree gradually decreases to 0”).
Regarding claim 17, Song discloses wherein: the three membership functions of the first input comprise variables of right lane, middle lane, and left lane; the three membership functions of the second input comprise variables of small, perfect, and large; the three membership functions of the third input comprise variables of shrinking, stable, and growing; and the four membership functions of the output comprise variables of right lane, middle lane, left lane, and no action (see Song paras “0182-0191” and “0251” “the processor 110 may identify factors that influence the lane-changing willingness, such as the lane-changing necessity, the lane-changing safety, a surrounding vehicle behavior, a road condition, or the like. For each factor, a fuzzy set may be defined, for example, dividing the lane-changing necessity into elements such as “small,” “relatively small,” “medium,” “relatively large,” “large,” or the like. The processor 110 may establish membership functions based on each element in the fuzzy set to describe the membership degree of the element in the corresponding fuzzy set. The membership functions may be linear, triangular, trapezoidal, or any other shape. For example, for the membership function of “high lane-changing necessity,” if the lane-changing necessity is 1, the value of the membership function is equal to or close to 1, if the lane-changing necessity is outside this range, the membership degree gradually decreases to 0”).
Regarding claim 19, Song discloses a method of changing lanes in a vehicle using an advanced driving assistance system, the method comprising: obtaining a first input with a sensor attached to the vehicle, the sensor in communication with a fuzzy inference system, the first input comprising a lane detection; obtaining a second input with the sensor, the second input comprising a distance, wherein the distance is a measurement between the vehicle and an object in front of the vehicle; obtaining a third input with the sensor, the third input comprising a relative distance (see Song paras “0014”, “0078”, “0146”, “0162” and “0167-0171” “establishing a prior probability distribution of a vehicle driving style of a side vehicle, including: obtaining vehicle driving data through an intelligent networked roadside sensor, and recording and counting the prior probability distribution of the vehicle driving style under different time periods and different road sections, wherein the vehicle driving style of the side vehicle includes an aggressive driving style and a non-aggressive driving style; step 2, outputting a lane-changing willingness through a lane-changing willingness determination module, including: collecting vehicle information of a specified vehicle (SV) and a surrounding vehicle through an on-board sensor, determining an original predetermined distance and a lane-changing predetermined distance, determining a lane-changing necessity and a lane-changing safety through a cumulative distribution function constructed by introducing an expectation and a variance, establishing a lane-changing willingness output model based on fuzzy logic” and “The original predetermined distance refers to a distance between the specified vehicle and a leading vehicle on the current lane... For example, the original predetermined distance may be a minimum distance between reference points of the specified vehicle and reference points of the leading vehicle at various future moments. As another example, the original predetermined distance may include distances between the reference points of the specified vehicle and the reference points of the leading vehicle at various future moments. The reference points may be manually preset values or system default values, such as the midpoint of the vehicle's front.”),
implementing the fuzzy inference system to apply a set of fuzzy logic rules to the first input, the second input, and the third input so as to generate an output (see Song paras “0014”, “0078”, “0126-0127”, “0131-0133”, “0146”, “0162” and “0167-0171” “establishing a prior probability distribution of a vehicle driving style of a side vehicle, including: obtaining vehicle driving data through an intelligent networked roadside sensor, and recording and counting the prior probability distribution of the vehicle driving style under different time periods and different road sections, wherein the vehicle driving style of the side vehicle includes an aggressive driving style and a non-aggressive driving style; step 2, outputting a lane-changing willingness through a lane-changing willingness determination module, including: collecting vehicle information of a specified vehicle (SV) and a surrounding vehicle through an on-board sensor, determining an original predetermined distance and a lane-changing predetermined distance, determining a lane-changing necessity and a lane-changing safety through a cumulative distribution function constructed by introducing an expectation and a variance, establishing a lane-changing willingness output model based on fuzzy logic” and “The original predetermined distance refers to a distance between the specified vehicle and a leading vehicle on the current lane... For example, the original predetermined distance may be a minimum distance between reference points of the specified vehicle and reference points of the leading vehicle at various future moments. As another example, the original predetermined distance may include distances between the reference points of the specified vehicle and the reference points of the leading vehicle at various future moments. The reference points may be manually preset values or system default values, such as the midpoint of the vehicle's front.”),
wherein the output comprises a recommendation on whether the vehicle should change lanes into the parallel lane (see Song paras “0014-0017”, “0077-0079”, “0158-0159”, “0162” and “0184-0186” “The lane-changing necessity refers to a result of evaluating the need for a lane-changing action based on a traffic condition and a driving requirement of the specified vehicle. In some embodiments, the lane-changing necessity may be expressed as a numerical value (e.g., a necessity degree, a necessity value, or the like), a grade (e.g., a necessity level), or the like.”),
the fuzzy inference system relaying the lane change recommendation to the vehicle, the vehicle applying the lane change recommendation by merging or taking no action, each of the fuzzy logic rules including an if statement and a then statement where the then statement describes the lane change recommendation that the vehicle should take based on the if statement (see Song paras “0084” and “0184-0186” “A lane-changing willingness output model may be established based on fuzzy logic. First, an input and an output of the fuzzy logic-based lane-changing willingness model may be designed. As shown in FIGS. 5(a), 5(b), and 5(c), a membership function of each of the lane-changing necessity, the lane-changing safety, and the lane-changing willingness may be constructed. Fuzzy sets for the input, i.e., the lane-changing necessity and the lane-changing safety may be {small, relatively small, medium, relatively large, large}, and the fuzzy set for the output, i.e., the lane-changing willingness, may be {weak, relatively weak, medium, relatively strong, strong}. Finally, a lane-changing willingness φLC may be obtained by performing a defuzzification operation according to a fuzzy rule table (e.g., as shown in Table 1) and a center of mass technique. Recommended fuzzy set classifications and fuzzy rule formulations are provided in Table 1 but not limited to the forms in the table”, “the processor 110 may initially establish the lane-changing willingness output model based on fuzzy logic and store the lane-changing willingness output model in the storage 140. The fuzzy logic is a process that uses a fuzzy set and a fuzzy rule to determine a parameter (e.g., the lane-changing willingness) related to lane-changing decision-making”, “A membership degree refers to a degree to which an element belongs to the corresponding fuzzy set. In some embodiments, the membership degree is a numerical value between 0 and 1” and “the processor 110 may identify factors that influence the lane-changing willingness, such as the lane-changing necessity, the lane-changing safety, a surrounding vehicle behavior, a road condition, or the like. For each factor, a fuzzy set may be defined, for example, dividing the lane-changing necessity into elements such as “small,” “relatively small,” “medium,” “relatively large,” “large,” or the like. The processor 110 may establish membership functions based on each element in the fuzzy set to describe the membership degree of the element in the corresponding fuzzy set” regarding obtaining vehicle and surrounding vehicle information through an on-board sensor and determining lane-changing necessity and lane-changing safety based on the obtained inputs. Song teaches establishing a lane-changing willingness output model based on fuzzy logic and generating a lane-changing willingness output through a fuzzy rule table and defuzzification operation. Also the claimed “if statement” and “then statement,” the fuzzy rule table and fuzzy rule formulations of the reference reasonably correspond to conditional decision logic under the broadest reasonable interpretation, where input conditions associated with lane-changing necessity and lane-changing safety correspond to the claimed “if statement,” and the resulting lane-changing willingness output corresponds to the claimed “then statement” describing the lane change recommendation to be taken. Further, the Song teaches mapping input variables to output variables corresponding to lane-changing willingness and performing a subsequent lane-changing decision-making process when the lane-changing willingness exceeds a threshold. Additionally, Song teaches that the lane-changing willingness output model is used to determine the tendency of a vehicle to perform a lane change and that fuzzy rules are used to determine parameters related to lane-changing decision-making. Thus, relaying a lane-change recommendation to the vehicle and applying the recommendation by performing a lane change when the determined conditions satisfy the fuzzy rule criteria, while otherwise refraining from performing the lane change operation),
but Song fails to explicitly teach wherein the lane detection is a detection of a parallel lane into which the vehicle may move in a change of lanes and wherein the relative distance is a measurement of a rate of change of a distance between the vehicle and an object detected in the parallel lane.
However, Tokoro teaches wherein the lane detection is a detection of a parallel lane into which the vehicle may move in a change of lanes and wherein the relative distance is a measurement of a rate of change of a distance between the vehicle and an object detected in the parallel lane (see Tokoro col 7, lines 20-27 “the object on the currently running lane is a ghost is determined using the fuzzy inference based on the parameters of the distance difference, the relative velocity difference, the angle, etc. between the detected object image on the currently running lane and the detected object image on the adjacent lane”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Song for methods for cooperative decision-making on lane-changing behaviors of autonomous vehicles based on Bayesian game “to carry out the object detection process without interruption and determine that the first detected object image is not a ghost, with a higher priority than the other determination conditions” as taught by Tokoro (col 7, lines 20-27) in order to improve safety for the vehicle and avoid any collision.
Claims 14-15 are rejected under 35 U.S.C. 103 as being unpatentable in view of Song et al (US 2024/0351615 A1), (hereinafter Song) in further view of Tokoro (US 6,323,802 B1) as applied to claim 13 above, in further view of Jung et al (US 2018/0164111 A1).
Regarding claim 14, Song fails to explicitly teach wherein the fuzzy inference system is a Mamdani fuzzy inference system.
However, Jung teaches wherein the fuzzy inference system is a Mamdani fuzzy inference system (see Jung para “0058” “A driving information estimation apparatus estimates a speed deviation using an ANFIS. In an example, the ANFIS is an artificial neural network combined with a fuzzy inference model, and may be implemented as an artificial neural network trained on an inference method using a fuzzy inference model based on a learning algorithm. The fuzzy inference model generates a fuzzy rule based on an input and output data set and includes, for example, a Mamdani-type fuzzy inference model and a Sugeno-type fuzzy inference model”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Song for methods for cooperative decision-making on lane-changing behaviors of autonomous vehicles based on Bayesian game “to guide information to drive the vehicle based on the speed profile, and outputting the guide information visually” as taught by Jung (para [0058]) in order to generate the speed deviations based on the driving characteristic and relative locations of the points using an adaptive neuro-fuzzy inference system.
Regarding claim 15, Song fails to explicitly teach wherein the fuzzy inference system is a Sugeno fuzzy inference system.
However, Jung teaches wherein the fuzzy inference system is a Sugeno fuzzy inference system (see Jung para “0058” “A driving information estimation apparatus estimates a speed deviation using an ANFIS. In an example, the ANFIS is an artificial neural network combined with a fuzzy inference model, and may be implemented as an artificial neural network trained on an inference method using a fuzzy inference model based on a learning algorithm. The fuzzy inference model generates a fuzzy rule based on an input and output data set and includes, for example, a Mamdani-type fuzzy inference model and a Sugeno-type fuzzy inference model”)
It would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Song for methods for cooperative decision-making on lane-changing behaviors of autonomous vehicles based on Bayesian game “to guide information to drive the vehicle based on the speed profile, and outputting the guide information visually” as taught by Jung (para [0058]) in order to generate the speed deviations based on the driving characteristic and relative locations of the points using an adaptive neuro-fuzzy inference system.
Allowable Subject Matter
Claim 18 is objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
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/HOSSAM M ABD EL LATIF/Examiner, Art Unit 3664
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